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- Author: Jeffrey Strickland
- Publisher: Lulu.com
- ISBN-10: 1365213846
- ISBN-13: 9781365213847
- Format: 15.2 x 22.9 x 3.8 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio, Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naïve and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.
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- Author: Jeffrey Strickland
- Publisher: Lulu.com
- ISBN-10: 1365213846
- ISBN-13: 9781365213847
- Format: 15.2 x 22.9 x 3.8 cm, hardcover
- Language: English English
This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio, Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naïve and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.
Reviews